This series of files compile analyses done for the global analysis of Chapter 1 (version of May 15th).

All analyses have been done with PRIMER-e 6 and R 3.6.3.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

We used data from subtidal ecosystems (see metadata files for more information). Only stations that have been sampled both for abiotic parameters and benthic species were included.

Selected variables for the analyses:

1. Data manipulation

For the following analyses, independant variables are habitat parameters and heavy metal concentrations, dependant variables are diversity indices. Variables have been standardized by mean and standard-deviation.

1.1. Identification of outliers

To identify stations that are not consistent with the others, we used the multivariate Cook’s Distance (CD) on the uncorrelated variables. A significative threshold of 4 times the mean of CD has been established.

0.5 mm community

We identified the following stations as general outliers:

  • stations 30, 127, 138, 144, 183, 228, 237 for habitat
  • stations 1, 11, 22, 25, 35, 127, 132, 139, 231 for metals

They have been deleted for the following analyses.

1 mm community

We identified the following stations as general outliers:

  • stations 72, 82, 107, 129, 144, 202, 249 for habitat
  • stations 106, 108, 110, 120, 127, 130, 139, 154, 232 for metals

They have been deleted for the following analyses.

1.2. Correlations between parameters

Correlations have been calculated with Spearman’s rank coefficient.

0.5 mm community

According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions:

  • chromium, iron and manganese (iron and manganese deleted)
  • copper, lead and zinc (copper and lead deleted)

We decided to exclude silt content, as it tends to drasticaly increase VIFs due to a marginal correlation with organic matter (\(R^{2}_{adj}\) = 0.21).

Correlation coefficients between habitat parameters (0.5 mm community subset)
  depth om gravel sand silt clay
depth 1 0.298 -0.26 0.209 0.497 -0.483
om 0.298 1 -0.504 -0.407 0.479 0.01
gravel -0.26 -0.504 1 0.019 -0.347 0.162
sand 0.209 -0.407 0.019 1 -0.007 -0.749
silt 0.497 0.479 -0.347 -0.007 1 -0.534
clay -0.483 0.01 0.162 -0.749 -0.534 1
Correlation coefficients between metals (0.5 mm community subset)
  arsenic cadmium chromium copper iron manganese mercury lead zinc
arsenic 1 0.732 0.631 0.713 0.405 0.62 0.711 0.865 0.808
cadmium 0.732 1 0.784 0.691 0.514 0.669 0.66 0.854 0.838
chromium 0.631 0.784 1 0.738 0.8 0.891 0.467 0.75 0.792
copper 0.713 0.691 0.738 1 0.571 0.738 0.612 0.843 0.928
iron 0.405 0.514 0.8 0.571 1 0.83 0.187 0.458 0.571
manganese 0.62 0.669 0.891 0.738 0.83 1 0.463 0.681 0.738
mercury 0.711 0.66 0.467 0.612 0.187 0.463 1 0.787 0.683
lead 0.865 0.854 0.75 0.843 0.458 0.681 0.787 1 0.928
zinc 0.808 0.838 0.792 0.928 0.571 0.738 0.683 0.928 1

1 mm community

According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions:

  • om and silt (silt deleted)
  • chromium, iron and manganese (iron and manganese deleted)
  • copper, lead and zinc (copper and lead deleted)
Correlation coefficients between habitat parameters (1 mm community subset)
  depth om gravel sand silt clay
depth 1 0.442 -0.026 -0.328 0.317 -0.097
om 0.442 1 -0.304 -0.798 0.841 -0.125
gravel -0.026 -0.304 1 0.124 -0.383 -0.023
sand -0.328 -0.798 0.124 1 -0.927 -0.12
silt 0.317 0.841 -0.383 -0.927 1 0.069
clay -0.097 -0.125 -0.023 -0.12 0.069 1
Correlation coefficients between metals (1 mm community subset)
  arsenic cadmium chromium copper iron manganese mercury lead zinc
arsenic 1 0.743 0.79 0.809 0.636 0.707 0.702 0.892 0.88
cadmium 0.743 1 0.772 0.639 0.541 0.662 0.669 0.833 0.812
chromium 0.79 0.772 1 0.858 0.823 0.902 0.667 0.837 0.892
copper 0.809 0.639 0.858 1 0.77 0.792 0.708 0.857 0.946
iron 0.636 0.541 0.823 0.77 1 0.869 0.412 0.595 0.753
manganese 0.707 0.662 0.902 0.792 0.869 1 0.573 0.705 0.79
mercury 0.702 0.669 0.667 0.708 0.412 0.573 1 0.844 0.743
lead 0.892 0.833 0.837 0.857 0.595 0.705 0.844 1 0.928
zinc 0.88 0.812 0.892 0.946 0.753 0.79 0.743 0.928 1

2. Permutational Analyses of Variance

Here, we explored the differences between the clusters defined in the script analyses_A. Results of univariate PermANOVAs on parameters, multivariate PermANOVA on the whole benthic community and basic statistics for each clusters are presented in the tables below. Variables have been standardized by mean and standard-deviation, and abundances were (log+1) transformed.

Habitat
Variable Cluster Significative groups of similar clusters (p > 0.05)
depth S
om S {cl1 cl3}
gravel S
sand S
silt S
clay S
Cluster 1
  Mean SD SE Median Min Max 95% CI
depth 8.474 6.439 1.088 7.700 4.000 39.300 2.133
om 1.584 1.568 0.265 1.257 0.187 8.260 0.519
gravel 0.004 0.009 0.002 0.000 0.000 0.035 0.003
sand 0.000 0.001 0.000 0.000 0.000 0.006 0.000
silt 0.008 0.014 0.002 0.001 0.000 0.081 0.005
clay 0.988 0.019 0.003 0.999 0.912 1.000 0.006
Cluster 2
  Mean SD SE Median Min Max 95% CI
depth 19.681 16.847 1.606 14.200 1.000 66.600 3.148
om 0.515 0.416 0.040 0.386 0.168 3.403 0.078
gravel 0.076 0.162 0.015 0.000 0.000 0.809 0.030
sand 0.837 0.202 0.019 0.896 0.000 1.001 0.038
silt 0.079 0.093 0.009 0.034 0.000 0.333 0.017
clay 0.008 0.050 0.005 0.000 0.000 0.520 0.009
Cluster 3
  Mean SD SE Median Min Max 95% CI
depth 30.413 19.269 1.957 27.000 1.000 77.000 3.835
om 1.758 0.957 0.097 1.512 0.316 4.415 0.191
gravel 0.001 0.006 0.001 0.000 0.000 0.060 0.001
sand 0.366 0.203 0.021 0.386 0.000 0.678 0.040
silt 0.588 0.158 0.016 0.576 0.322 0.942 0.031
clay 0.046 0.110 0.011 0.000 0.000 0.497 0.022
Metals
Variable Cluster Significative groups of similar clusters (p > 0.05)
arsenic S
cadmium S
chromium S
copper S
iron S
manganese S
mercury S
lead S
zinc S
Cluster 1
  Mean SD SE Median Min Max 95% CI
arsenic 5.157 3.173 0.362 4.300 2.20 21.30 0.709
cadmium 0.157 0.026 0.003 0.160 0.12 0.27 0.006
chromium 73.342 15.507 1.767 73.000 51.20 143.30 3.464
copper 17.121 4.405 0.502 16.300 10.40 32.40 0.984
iron 62534.241 9645.914 1099.254 61157.800 45202.20 98544.60 2154.498
manganese 1623.165 802.568 91.461 1351.100 723.80 5962.19 179.261
mercury 0.034 0.031 0.004 0.028 0.00 0.25 0.007
lead 7.154 1.810 0.206 7.100 3.59 12.20 0.404
zinc 76.361 16.687 1.902 72.200 47.30 141.40 3.727
Cluster 2
  Mean SD SE Median Min Max 95% CI
arsenic 2.304 0.569 0.057 2.300 0.80 3.700 0.113
cadmium 0.114 0.037 0.004 0.110 0.03 0.230 0.007
chromium 45.932 18.413 1.860 42.050 10.90 125.000 3.646
copper 6.556 3.079 0.311 6.150 2.20 19.400 0.610
iron 49771.330 25672.466 2593.311 44025.375 14089.92 188857.220 5082.795
manganese 776.206 290.642 29.359 710.250 251.67 1696.200 57.543
mercury 0.010 0.009 0.001 0.011 0.00 0.037 0.002
lead 3.362 1.171 0.118 3.125 1.02 6.200 0.232
zinc 42.884 8.845 0.893 44.450 15.90 61.600 1.751
0.5 mm community
Variable Cluster Significative groups of similar clusters (p > 0.05)
S S {cl2 cl3}
N S {cl2 cl3}
H S {cl1 cl2}, {cl1 cl3}
J S
ALL SPECIES S
Cluster 1
  Mean SD SE Median Min Max 95% CI
S 27.812 3.781 0.945 28.500 21.000 34.000 1.853
N 1411.188 410.971 102.743 1433.000 636.000 2103.000 201.372
H 1.823 0.268 0.067 1.882 1.246 2.247 0.131
J 0.550 0.081 0.020 0.559 0.409 0.682 0.040
Cluster 2
  Mean SD SE Median Min Max 95% CI
S 12.197 6.077 0.778 12.000 1 35.000 1.525
N 110.131 175.663 22.491 56.000 1 941.000 44.082
H 1.619 0.612 0.078 1.623 0 2.737 0.154
J 0.683 0.213 0.027 0.706 0 1.000 0.053
Cluster 3
  Mean SD SE Median Min Max 95% CI
S 13.270 4.871 0.516 13.000 4.000 24.000 1.012
N 92.180 71.364 7.565 75.000 4.000 450.000 14.826
H 1.896 0.430 0.046 1.971 0.975 2.577 0.089
J 0.758 0.129 0.014 0.781 0.402 1.000 0.027
1 mm community
Variable Cluster Significative groups of similar clusters (p > 0.05)
S S
N {cl1 cl3}, {cl1 cl2 cl4}, {cl1 cl4 cl5}
H S {cl2 cl3}
J S {cl2 cl3}, {cl1 cl4 cl5}
ALL SPECIES S
Cluster 1
  Mean SD SE Median Min Max 95% CI
S 7.244 4.231 0.456 7.000 1 19.000 0.894
N 35.314 48.890 5.272 21.000 1 321.000 10.333
H 1.384 0.607 0.065 1.400 0 2.646 0.128
J 0.760 0.205 0.022 0.818 0 1.000 0.043
Cluster 2
  Mean SD SE Median Min Max 95% CI
S 5.154 3.313 0.919 5.000 1 12.000 1.801
N 28.846 27.763 7.700 19.000 2 96.000 15.092
H 0.977 0.701 0.194 1.012 0 2.138 0.381
J 0.585 0.297 0.082 0.760 0 0.973 0.161
Cluster 3
  Mean SD SE Median Min Max 95% CI
S 2.143 1.167 0.312 2.000 0 4.00 0.611
N 12.357 20.171 5.391 6.000 0 77.00 10.566
H 0.585 0.463 0.124 0.615 0 1.33 0.243
J 0.611 0.424 0.113 0.843 0 1.00 0.222
Cluster 4
  Mean SD SE Median Min Max 95% CI
S 8.921 4.733 0.768 9.000 2.000 19.000 1.505
N 64.474 110.452 17.918 33.000 2.000 674.000 35.118
H 1.511 0.530 0.086 1.482 0.516 2.573 0.168
J 0.764 0.176 0.028 0.818 0.235 1.000 0.056
Cluster 5
  Mean SD SE Median Min Max 95% CI
S 9.255 3.851 0.539 9.000 1 19.000 1.057
N 38.608 29.935 4.192 33.000 1 143.000 8.216
H 1.745 0.526 0.074 1.899 0 2.443 0.144
J 0.805 0.194 0.027 0.857 0 1.000 0.053

3. Similarity and characteristic species

Let’s have a look at the \(\beta\) diversity within these clusters.

0.5 mm community

Results of the PERMDISP routine are shown below (mean and SE of the deviation from centroid for each group, i.e. multivariate dispersion), along with the mean Bray-Curtis dissimilarity for each cluster. Abundances were (log+1) transformed and PRIMER was used to do the PERMDISP.

Mean within-group Bray-Curtis dissimilarity for each cluster
  Mean deviation SE of deviation Mean BC dissimilarity
cluster 1 25.8 1.19 0.377
cluster 2 59.7 0.89 0.848
cluster 3 48.8 1.02 0.694

Significative differences in dispersion have been detected by the PERMDISP and the pairwise tests between each cluster pairs (p < 0.05).

The following analyses allowed to detect species as characteristic of each cluster. We used results from PRIMER to justify further their choice.

##                             cluster indicator_value probability
## capitella_sp                      1          0.9861       0.001
## nephtys_sp                        1          0.9861       0.001
## prionospio_steenstrupi            1          0.9692       0.001
## phyllodoce_groenlandica           1          0.9664       0.001
## cirratulidae_spp                  1          0.9331       0.001
## phoronida                         1          0.9288       0.001
## scoloplos_armiger                 1          0.8889       0.001
## sarsicytheridea_sp                1          0.8706       0.001
## polychaeta                        1          0.8100       0.001
## limecola_balthica                 1          0.7949       0.001
## sertulariidae_spp                 1          0.7531       0.001
## eteone_sp                         1          0.7064       0.001
## bipalponephtys_neotena            1          0.6982       0.001
## campanulariidae_spp               1          0.6946       0.001
## harpacticoida                     1          0.5711       0.001
## euchone_analis                    1          0.5625       0.001
## pholoe_longa                      1          0.5096       0.001
## podocopida                        1          0.4323       0.001
## glycera_dibranchiata              1          0.4297       0.001
## hediste_diversicolor              1          0.4257       0.001
## tharyx_sp                         1          0.3750       0.001
## diastylis_sculpta                 1          0.3651       0.001
## phoxocephalus_holbolli            1          0.3346       0.006
## pholoe_minuta_tecta               1          0.3282       0.001
## praxillella_praetermissa          1          0.3099       0.001
## microphthalmus_sczelkowii         1          0.3009       0.001
## aricidea_sp                       1          0.2998       0.001
## sabellidae_spp                    1          0.2959       0.003
## solenoidea                        1          0.2878       0.001
## pholoe_sp                         1          0.2770       0.047
## microphthalmus_sp                 1          0.2500       0.001
## pontoporeia_femorata              1          0.2477       0.019
## axinopsida_orbiculata             1          0.2457       0.005
## eucratea_loricata                 1          0.2286       0.004
## bivalvia                          1          0.2090       0.001
## cylichna_alba                     1          0.1875       0.003
## harmothoe_imbricata               1          0.1875       0.002
## eudendriidae_spp                  1          0.1724       0.004
## gammaridea                        1          0.1680       0.003
## hemicythere_villosa               1          0.1624       0.002
## spio_filicornis                   1          0.1403       0.019
## eteone_longa                      1          0.1250       0.005
## hartmania_moorei                  1          0.1250       0.009
## macoma_sp                         1          0.1250       0.004
## monticellina_sp                   1          0.1250       0.013
## pherusa_sp                        1          0.1250       0.006
## scoletoma_tetraura                1          0.1250       0.009
## capitellidae_spp                  1          0.1193       0.005
## brachyura                         1          0.1183       0.010
## echinarachnius_parma              2          0.5050       0.001
## nematoda                          2          0.3257       0.009
## spisula_solidissima               2          0.2951       0.003
## crenella_decussata                2          0.2317       0.002
## annelida                          2          0.2131       0.001
## polygordius_sp                    2          0.1894       0.016
## nephtys_caeca                     2          0.1754       0.031
## orchomenella_minuta               2          0.1003       0.049
## halacaridae_spp                   2          0.0984       0.032
## ophiura_robusta                   2          0.0984       0.035
## lepeta_caeca                      2          0.0912       0.038
## boreochiton_ruber                 2          0.0656       0.046
## macoma_calcarea                   3          0.6715       0.001
## eudorellopsis_integra             3          0.6687       0.001
## ennucula_tenuis                   3          0.4903       0.001
## leucon_leucon_nasicoides          3          0.4655       0.001
## goniada_maculata                  3          0.4399       0.001
## protomedeia_grandimana            3          0.3599       0.002
## ostracoda                         3          0.3403       0.001
## nephtys_incisa                    3          0.3293       0.001
## thyasira_gouldi                   3          0.3186       0.002
## akanthophoreus_gracilis           3          0.3118       0.001
## amphipoda                         3          0.2632       0.019
## quasimelita_formosa               3          0.2374       0.009
## aceroides_aceroides_latipes       3          0.2267       0.006
## chaetodermatida                   3          0.1566       0.026
## sipuncula                         3          0.1410       0.048
## 
## Sum of probabilities                 =  81.176 
## 
## Sum of Indicator Values              =  35.53 
## 
## Sum of Significant Indicator Values  =  29.09 
## 
## Number of Significant Indicators     =  76 
## 
## Significant Indicator Distribution
## 
##  1  2  3 
## 49 12 15
SIMPER results between clusters 1 and 2 (mean between-group Bray-Curtis dissimilarity: 0.925)
  average sd ratio ava avb cumsum
bipalponephtys_neotena 0.0735 0.0147 4.99 6.17 0.278 0.0794
nephtys_sp 0.0726 0.0124 5.85 5.92 0.0682 0.158
prionospio_steenstrupi 0.0494 0.0129 3.84 4.09 0.13 0.211
phoronida 0.0439 0.0162 2.71 3.64 0.0341 0.259
scoloplos_armiger 0.0437 0.0189 2.32 3.7 0.202 0.306
capitella_sp 0.0392 0.0111 3.53 3.23 0.0455 0.348
phyllodoce_groenlandica 0.0382 0.0101 3.8 3.23 0.113 0.39
cirratulidae_spp 0.0298 0.0122 2.45 2.42 0.0114 0.422
sarsicytheridea_sp 0.027 0.0139 1.95 2.26 0.0114 0.451
limecola_balthica 0.0251 0.0154 1.63 2.05 0.0455 0.478
harpacticoida 0.0211 0.0129 1.63 2.21 0.902 0.501
eteone_sp 0.0192 0.0129 1.49 1.62 0.0768 0.522
phoxocephalus_holbolli 0.0169 0.0133 1.27 1.36 1.02 0.54
euchone_analis 0.0164 0.017 0.964 1.43 0 0.558
nematoda 0.0151 0.02 0.756 0 1.21 0.574
pholoe_sp 0.0149 0.0145 1.03 1.19 0.258 0.59
pholoe_longa 0.0145 0.0141 1.03 1.21 0.0965 0.606
echinarachnius_parma 0.0134 0.0139 0.966 0 1.1 0.62
pholoe_minuta_tecta 0.0123 0.0162 0.757 0.959 0.137 0.633
podocopida 0.0112 0.0154 0.727 0.944 0.0114 0.646
sabellidae_spp 0.0108 0.0152 0.708 0.909 0.0114 0.657
pontoporeia_femorata 0.0104 0.013 0.8 0.804 0.0114 0.668
hediste_diversicolor 0.0102 0.00999 1.02 0.842 0.102 0.679
microphthalmus_sczelkowii 0.0094 0.0143 0.657 0.761 0.0294 0.69
diastylis_sculpta 0.00927 0.0122 0.758 0.783 0.0114 0.7
SIMPER results between clusters 1 and 3 (mean between-group Bray-Curtis dissimilarity: 0.904)
  average sd ratio ava avb cumsum
nephtys_sp 0.07 0.0112 6.23 5.92 0.0156 0.0774
prionospio_steenstrupi 0.0487 0.0107 4.56 4.09 0 0.131
bipalponephtys_neotena 0.0457 0.0192 2.38 6.17 2.39 0.182
scoloplos_armiger 0.0438 0.0176 2.49 3.7 0 0.23
phoronida 0.0423 0.0155 2.73 3.64 0 0.277
capitella_sp 0.0379 0.0102 3.73 3.23 0 0.319
phyllodoce_groenlandica 0.0379 0.0086 4.4 3.23 0 0.361
cirratulidae_spp 0.0286 0.0116 2.47 2.42 0 0.393
sarsicytheridea_sp 0.026 0.0133 1.95 2.26 0 0.421
limecola_balthica 0.0244 0.0149 1.64 2.05 0 0.448
harpacticoida 0.0215 0.012 1.78 2.21 0.519 0.472
eudorellopsis_integra 0.0191 0.0161 1.18 0.0687 1.68 0.493
eteone_sp 0.0187 0.0124 1.5 1.62 0.0234 0.514
macoma_calcarea 0.0158 0.0112 1.41 0.0687 1.4 0.531
euchone_analis 0.0157 0.0163 0.964 1.43 0 0.549
phoxocephalus_holbolli 0.0153 0.0128 1.19 1.36 0.156 0.566
pholoe_sp 0.0143 0.0118 1.21 1.19 0.702 0.582
pholoe_longa 0.014 0.0138 1.01 1.21 0.0297 0.597
pontoporeia_femorata 0.0124 0.0129 0.965 0.804 0.605 0.611
sabellidae_spp 0.0114 0.0149 0.768 0.909 0.232 0.623
pholoe_minuta_tecta 0.0112 0.0156 0.716 0.959 0 0.636
podocopida 0.0107 0.0147 0.724 0.944 0 0.648
hediste_diversicolor 0.00966 0.00911 1.06 0.842 0.169 0.658
leucon_leucon_nasicoides 0.00947 0.0124 0.765 0 0.828 0.669
diastylis_sculpta 0.00943 0.0117 0.806 0.783 0.144 0.679
protomedeia_grandimana 0.0092 0.011 0.836 0 0.803 0.689
axinopsida_orbiculata 0.00915 0.011 0.831 0.677 0.322 0.7
SIMPER results between clusters 2 and 3 (mean between-group Bray-Curtis dissimilarity: 0.917)
  average sd ratio ava avb cumsum
bipalponephtys_neotena 0.065 0.0407 1.6 0.278 2.39 0.0709
eudorellopsis_integra 0.0484 0.0439 1.1 0.0294 1.68 0.124
nematoda 0.0427 0.0502 0.85 1.21 0.676 0.17
macoma_calcarea 0.0396 0.0335 1.18 0.289 1.4 0.213
echinarachnius_parma 0.0331 0.0368 0.901 1.1 0.15 0.25
phoxocephalus_holbolli 0.0289 0.034 0.85 1.02 0.156 0.281
harpacticoida 0.0277 0.0291 0.952 0.902 0.519 0.311
protomedeia_grandimana 0.0265 0.0344 0.768 0.225 0.803 0.34
leucon_leucon_nasicoides 0.0232 0.0308 0.753 0.0114 0.828 0.365
ennucula_tenuis 0.0216 0.0246 0.877 0.0774 0.774 0.389
pholoe_sp 0.0211 0.0228 0.926 0.258 0.702 0.412
spisula_solidissima 0.0209 0.0386 0.542 0.753 0 0.435
pontoporeia_femorata 0.0186 0.0339 0.549 0.0114 0.605 0.455
amphipoda 0.0156 0.021 0.741 0.185 0.452 0.472
goniada_maculata 0.0149 0.0202 0.736 0.0114 0.523 0.488
maldanidae_spp 0.0148 0.0282 0.523 0.0114 0.529 0.504
ostracoda 0.0138 0.0208 0.664 0.0455 0.507 0.52
thyasira_gouldi 0.0134 0.0224 0.599 0.0114 0.498 0.534
akanthophoreus_gracilis 0.0131 0.0212 0.619 0.0227 0.506 0.548
nephtys_incisa 0.0109 0.0164 0.668 0.0341 0.371 0.56
oligochaeta 0.0107 0.0258 0.415 0.166 0.266 0.572
polynoidae_spp 0.0106 0.0181 0.584 0.0638 0.341 0.584
axinopsida_orbiculata 0.0103 0.0222 0.462 0.0341 0.322 0.595
crenella_decussata 0.00963 0.02 0.482 0.328 0.0201 0.605
mytilus_sp 0.00954 0.0219 0.436 0.341 0.0824 0.616
thracia_septentrionalis 0.0094 0.0201 0.467 0.19 0.183 0.626
caprella_septentrionalis 0.00934 0.0273 0.342 0.33 0.0549 0.636
quasimelita_formosa 0.00927 0.0175 0.53 0.0294 0.332 0.646
polygordius_sp 0.00916 0.0207 0.443 0.359 0 0.656
cistenides_granulata 0.00881 0.0162 0.543 0.217 0.145 0.666
nephtys_caeca 0.00802 0.0146 0.55 0.226 0.0435 0.675
aceroides_aceroides_latipes 0.00741 0.0149 0.497 0.0114 0.279 0.683
hediste_diversicolor 0.0073 0.0182 0.401 0.102 0.169 0.691
ameritella_agilis 0.00717 0.0166 0.432 0.192 0.0591 0.698
1 mm community

Results of the PERMDISP routine are shown below (mean and SE of the deviation from centroid for each group, i.e. multivariate dispersion), along with the mean Bray-Curtis dissimilarity for each cluster. Abundances were (log+1) transformed and PRIMER was used to do the PERMDISP.

Mean within-group Bray-Curtis dissimilarity for each cluster
  Mean deviation SE of deviation Mean BC dissimilarity
cluster 1 65.5 0.57 0.928
cluster 2 37 3.31 0.547
cluster 3 34.3 3.22 0.51
cluster 4 53.8 0.67 0.765
cluster 5 46 1.26 0.655

Significative differences in dispersion have been detected by the PERMDISP and the pairwise tests between each cluster pairs (p < 0.05), except clusters 2 and 3 (p = 0.6).

The following analyses allowed to detect species as characteristic of each cluster. We used results from PRIMER to justify further their choice.

##                          cluster indicator_value probability
## cistenides_granulata           1          0.2393       0.007
## crenella_decussata             1          0.1781       0.014
## mesodesma_arctatum             2          0.9709       0.001
## nephtys_caeca                  2          0.2328       0.006
## harpinia_propinqua             2          0.0802       0.032
## echinarachnius_parma           3          0.4747       0.001
## macoma_calcarea                4          0.5476       0.001
## bipalponephtys_neotena         4          0.3216       0.002
## pholoe_sp                      4          0.2173       0.020
## lamprops_fuscatus              4          0.1880       0.005
## diastylis_rathkei              4          0.1416       0.008
## axinopsida_orbiculata          4          0.1245       0.049
## hediste_diversicolor           4          0.1232       0.048
## retusa_obtusa                  4          0.1098       0.031
## holothuroidea                  4          0.1053       0.043
## leucon_leucon_nasicoides       5          0.6079       0.001
## protomedeia_grandimana         5          0.5819       0.001
## goniada_maculata               5          0.5556       0.001
## eudorellopsis_integra          5          0.5501       0.001
## ennucula_tenuis                5          0.4939       0.001
## maldanidae_spp                 5          0.4097       0.001
## nephtys_incisa                 5          0.3554       0.001
## quasimelita_formosa            5          0.3433       0.001
## thyasira_gouldi                5          0.3333       0.002
## polynoidae_spp                 5          0.2147       0.006
## chaetodermatida                5          0.1883       0.004
## sipuncula                      5          0.1707       0.008
## pontoporeia_femorata           5          0.1642       0.025
## oligochaeta                    5          0.1476       0.043
## eudorella_emarginata           5          0.1176       0.021
## philomedes_sp                  5          0.0784       0.037
## 
## Sum of probabilities                 =  72.596 
## 
## Sum of Indicator Values              =  14.88 
## 
## Sum of Significant Indicator Values  =  9.37 
## 
## Number of Significant Indicators     =  31 
## 
## Significant Indicator Distribution
## 
##  1  2  3  4  5 
##  2  3  1  9 16
SIMPER results between clusters 1 and 2 (mean between-group Bray-Curtis dissimilarity: 0.932)
  average sd ratio ava avb cumsum
mesodesma_arctatum 0.205 0.113 1.81 0.073 2.44 0.22
echinarachnius_parma 0.0973 0.0922 1.06 0.501 1.31 0.324
cistenides_granulata 0.0397 0.0704 0.565 0.52 0 0.367
nephtys_caeca 0.0364 0.0419 0.869 0.18 0.38 0.406
phoxocephalus_holbolli 0.0306 0.0572 0.535 0.333 0.162 0.439
strongylocentrotus_sp 0.0251 0.0516 0.486 0.263 0.116 0.466
macoma_calcarea 0.0237 0.0501 0.474 0.251 0.162 0.491
nematoda 0.0207 0.0541 0.382 0.249 0.0578 0.513
ameritella_agilis 0.0144 0.0404 0.355 0.157 0.0578 0.529
crenella_decussata 0.0143 0.0348 0.411 0.238 0 0.544
protomedeia_grandimana 0.0142 0.0473 0.301 0.24 0 0.559
orchomenella_minuta 0.0141 0.035 0.403 0.0611 0.149 0.574
limecola_balthica 0.0135 0.0393 0.343 0.174 0 0.589
pholoe_sp 0.0111 0.025 0.445 0.0516 0.116 0.601
harpinia_propinqua 0.011 0.0359 0.307 0.00815 0.207 0.613
polynoidae_spp 0.0105 0.0273 0.385 0.107 0.0578 0.624
amphipholis_squamata 0.00981 0.0403 0.243 0.122 0 0.634
scoloplos_armiger 0.0094 0.0383 0.245 0.139 0 0.644
caprella_septentrionalis 0.00939 0.0366 0.256 0.212 0 0.655
nephtys_incisa 0.0084 0.0221 0.38 0.0619 0.0578 0.664
nephtys_ciliata 0.00826 0.0289 0.286 0 0.116 0.672
harmothoe_imbricata 0.00797 0.0306 0.261 0.0795 0 0.681
phyllodoce_mucosa 0.00781 0.0247 0.317 0.0455 0.0578 0.689
ophiura_robusta 0.00775 0.0321 0.241 0.185 0 0.698
SIMPER results between clusters 1 and 3 (mean between-group Bray-Curtis dissimilarity: 0.91)
  average sd ratio ava avb cumsum
echinarachnius_parma 0.185 0.148 1.25 0.501 1.85 0.203
cistenides_granulata 0.0532 0.0876 0.607 0.52 0.0495 0.261
strongylocentrotus_sp 0.0363 0.07 0.519 0.263 0.157 0.301
phoxocephalus_holbolli 0.0276 0.0608 0.455 0.333 0 0.332
limecola_balthica 0.0224 0.0533 0.42 0.174 0.0495 0.356
scoloplos_armiger 0.0206 0.0586 0.351 0.139 0.0785 0.379
amphipholis_squamata 0.0205 0.0576 0.356 0.122 0.099 0.401
nephtys_caeca 0.0194 0.0441 0.44 0.18 0 0.423
nematoda 0.0192 0.0635 0.302 0.249 0 0.444
harmothoe_imbricata 0.0179 0.0494 0.362 0.0795 0.0785 0.463
protomedeia_grandimana 0.0172 0.0553 0.31 0.24 0 0.482
crenella_decussata 0.0171 0.0411 0.416 0.238 0 0.501
macoma_calcarea 0.0166 0.0419 0.395 0.251 0 0.519
ciliatocardium_ciliatum 0.0162 0.0518 0.313 0.0551 0.115 0.537
ameritella_agilis 0.014 0.0495 0.283 0.157 0 0.552
mya_arenaria 0.0137 0.0326 0.419 0.101 0.0495 0.567
psammonyx_nobilis 0.0124 0.0455 0.272 0.0637 0.0495 0.581
diastylis_sculpta 0.0119 0.0397 0.3 0.0852 0.0495 0.594
orchomenella_minuta 0.0119 0.0424 0.28 0.0611 0.0495 0.607
nephtys_bucera 0.0115 0.0292 0.394 0.0163 0.099 0.62
ophelia_limacina 0.0114 0.0309 0.368 0.0748 0.0495 0.632
caprella_septentrionalis 0.0107 0.0413 0.26 0.212 0 0.644
arrhoges_occidentalis 0.00918 0.0347 0.265 0.0211 0.0495 0.654
hiatella_arctica 0.00918 0.033 0.278 0.127 0 0.664
mesodesma_arctatum 0.00888 0.0446 0.199 0.073 0 0.674
ophiura_robusta 0.00878 0.0359 0.244 0.185 0 0.684
mytilus_sp 0.00724 0.0333 0.217 0.102 0 0.692
glycera_dibranchiata 0.00711 0.0353 0.201 0.0408 0 0.699
SIMPER results between clusters 1 and 4 (mean between-group Bray-Curtis dissimilarity: 0.947)
  average sd ratio ava avb cumsum
macoma_calcarea 0.0964 0.0717 1.34 0.251 1.62 0.102
bipalponephtys_neotena 0.0543 0.0666 0.814 0.0434 1.09 0.159
eudorellopsis_integra 0.0468 0.0636 0.736 0.0422 0.835 0.208
echinarachnius_parma 0.0331 0.0481 0.687 0.501 0.239 0.243
cistenides_granulata 0.0309 0.0514 0.602 0.52 0.0578 0.276
pholoe_sp 0.0304 0.0421 0.723 0.0516 0.576 0.308
pontoporeia_femorata 0.0279 0.0554 0.504 0.075 0.56 0.338
ennucula_tenuis 0.0261 0.0393 0.663 0.0748 0.38 0.365
phoxocephalus_holbolli 0.0218 0.0384 0.567 0.333 0.181 0.388
nephtys_caeca 0.0186 0.0328 0.568 0.18 0.16 0.408
axinopsida_orbiculata 0.0185 0.0398 0.463 0.0326 0.335 0.427
thracia_septentrionalis 0.0179 0.0433 0.415 0.127 0.235 0.446
protomedeia_grandimana 0.0148 0.0389 0.381 0.24 0.0971 0.462
diastylis_sculpta 0.0146 0.0338 0.431 0.0852 0.207 0.477
strongylocentrotus_sp 0.0143 0.0384 0.373 0.263 0 0.492
lamprops_fuscatus 0.0129 0.0241 0.536 0.0469 0.233 0.506
polynoidae_spp 0.0126 0.0284 0.445 0.107 0.191 0.519
diastylis_rathkei 0.0124 0.0328 0.377 0 0.237 0.532
nematoda 0.0123 0.0412 0.297 0.249 0 0.545
praxillella_praetermissa 0.0117 0.0438 0.268 0.0163 0.158 0.558
scoloplos_armiger 0.0117 0.0362 0.324 0.139 0.0713 0.57
crenella_decussata 0.0112 0.0278 0.404 0.238 0 0.582
goniada_maculata 0.0109 0.027 0.405 0.0422 0.181 0.593
hediste_diversicolor 0.0109 0.0227 0.483 0.0292 0.225 0.605
ameritella_agilis 0.0105 0.0325 0.323 0.157 0.0289 0.616
limecola_balthica 0.00998 0.0286 0.348 0.174 0 0.627
scoletoma_fragilis 0.00924 0.0252 0.367 0.0508 0.16 0.636
thyasira_sp 0.00911 0.0354 0.257 0.0271 0.11 0.646
sabellidae_spp 0.0091 0.0323 0.281 0.063 0.199 0.656
thyasira_gouldi 0.00839 0.0238 0.353 0.0374 0.115 0.664
glycera_sp 0.00824 0.0284 0.29 0.0408 0.0912 0.673
nephtys_incisa 0.00805 0.0194 0.414 0.0619 0.0836 0.682
caprella_septentrionalis 0.0078 0.031 0.252 0.212 0 0.69
oligochaeta 0.0074 0.026 0.285 0.0129 0.182 0.698
SIMPER results between clusters 1 and 5 (mean between-group Bray-Curtis dissimilarity: 0.951)
  average sd ratio ava avb cumsum
eudorellopsis_integra 0.0732 0.0569 1.29 0.0422 1.65 0.077
protomedeia_grandimana 0.0618 0.0473 1.31 0.24 1.36 0.142
ennucula_tenuis 0.0471 0.0433 1.09 0.0748 1.06 0.192
leucon_leucon_nasicoides 0.0404 0.0437 0.925 0 0.898 0.234
maldanidae_spp 0.0391 0.0542 0.722 0.0597 0.879 0.275
goniada_maculata 0.039 0.0318 1.23 0.0422 0.823 0.316
macoma_calcarea 0.0365 0.0328 1.12 0.251 0.77 0.355
thyasira_gouldi 0.0288 0.039 0.739 0.0374 0.648 0.385
pontoporeia_femorata 0.0283 0.0452 0.626 0.075 0.552 0.415
bipalponephtys_neotena 0.0281 0.0314 0.894 0.0434 0.653 0.444
cistenides_granulata 0.0255 0.0366 0.697 0.52 0.165 0.471
quasimelita_formosa 0.0252 0.0368 0.686 0.0292 0.565 0.497
nephtys_incisa 0.0242 0.0285 0.847 0.0619 0.536 0.523
polynoidae_spp 0.0213 0.0281 0.757 0.107 0.431 0.545
echinarachnius_parma 0.0209 0.0349 0.598 0.501 0 0.567
nematoda 0.0152 0.0345 0.44 0.249 0.16 0.583
phoxocephalus_holbolli 0.0145 0.03 0.483 0.333 0.0136 0.598
oligochaeta 0.0142 0.0318 0.447 0.0129 0.328 0.613
pholoe_sp 0.0138 0.0196 0.705 0.0516 0.304 0.628
nephtys_caeca 0.0125 0.0284 0.441 0.18 0.0969 0.641
strongylocentrotus_sp 0.0116 0.0288 0.401 0.263 0.0136 0.653
axinopsida_orbiculata 0.0105 0.0281 0.374 0.0326 0.199 0.664
sipuncula 0.00964 0.0185 0.521 0.0292 0.221 0.674
crenella_decussata 0.00954 0.0223 0.428 0.238 0.0136 0.684
protomedeia_fasciata 0.00817 0.0246 0.333 0 0.171 0.693
SIMPER results between clusters 2 and 3 (mean between-group Bray-Curtis dissimilarity: 0.814)
  average sd ratio ava avb cumsum
mesodesma_arctatum 0.296 0.107 2.77 2.44 0 0.363
echinarachnius_parma 0.181 0.137 1.32 1.31 1.85 0.586
nephtys_caeca 0.0471 0.0497 0.946 0.38 0 0.643
strongylocentrotus_sp 0.0274 0.0503 0.544 0.116 0.157 0.677
orchomenella_minuta 0.0176 0.0346 0.508 0.149 0.0495 0.699
SIMPER results between clusters 2 and 4 (mean between-group Bray-Curtis dissimilarity: 0.954)
  average sd ratio ava avb cumsum
mesodesma_arctatum 0.163 0.0797 2.04 2.44 0 0.171
macoma_calcarea 0.105 0.0705 1.49 0.162 1.62 0.281
echinarachnius_parma 0.0765 0.0758 1.01 1.31 0.239 0.361
bipalponephtys_neotena 0.0576 0.0699 0.824 0 1.09 0.421
eudorellopsis_integra 0.0498 0.0672 0.742 0 0.835 0.474
pholoe_sp 0.0339 0.0419 0.808 0.116 0.576 0.509
nephtys_caeca 0.0283 0.0322 0.878 0.38 0.16 0.539
ennucula_tenuis 0.027 0.0413 0.653 0 0.38 0.567
pontoporeia_femorata 0.0265 0.0556 0.477 0 0.56 0.595
axinopsida_orbiculata 0.0191 0.0419 0.455 0 0.335 0.615
phoxocephalus_holbolli 0.0156 0.0328 0.474 0.162 0.181 0.631
thracia_septentrionalis 0.0155 0.0451 0.343 0 0.235 0.647
diastylis_rathkei 0.0133 0.0344 0.385 0 0.237 0.661
polynoidae_spp 0.0131 0.0287 0.457 0.0578 0.191 0.675
lamprops_fuscatus 0.0123 0.0245 0.502 0 0.233 0.688
diastylis_sculpta 0.0116 0.0295 0.392 0 0.207 0.7
SIMPER results between clusters 2 and 5 (mean between-group Bray-Curtis dissimilarity: 0.98)
  average sd ratio ava avb cumsum
mesodesma_arctatum 0.122 0.0499 2.45 2.44 0 0.125
eudorellopsis_integra 0.0792 0.0587 1.35 0 1.65 0.206
protomedeia_grandimana 0.0652 0.0478 1.36 0 1.36 0.272
echinarachnius_parma 0.0593 0.0603 0.982 1.31 0 0.333
ennucula_tenuis 0.0513 0.0457 1.12 0 1.06 0.385
leucon_leucon_nasicoides 0.0431 0.0454 0.95 0 0.898 0.429
maldanidae_spp 0.0411 0.0574 0.716 0 0.879 0.471
goniada_maculata 0.041 0.0329 1.24 0.0578 0.823 0.513
macoma_calcarea 0.0389 0.0344 1.13 0.162 0.77 0.553
thyasira_gouldi 0.0302 0.0413 0.732 0 0.648 0.583
bipalponephtys_neotena 0.0295 0.0327 0.903 0 0.653 0.614
pontoporeia_femorata 0.0284 0.0465 0.61 0 0.552 0.642
quasimelita_formosa 0.0266 0.0388 0.685 0 0.565 0.67
nephtys_incisa 0.0257 0.0298 0.862 0.0578 0.536 0.696
SIMPER results between clusters 3 and 4 (mean between-group Bray-Curtis dissimilarity: 0.968)
  average sd ratio ava avb cumsum
echinarachnius_parma 0.142 0.0978 1.45 1.85 0.239 0.147
macoma_calcarea 0.135 0.0825 1.64 0 1.62 0.286
bipalponephtys_neotena 0.0676 0.0805 0.84 0 1.09 0.356
eudorellopsis_integra 0.0596 0.0793 0.751 0 0.835 0.418
pholoe_sp 0.0371 0.0513 0.722 0 0.576 0.456
ennucula_tenuis 0.0336 0.0504 0.666 0 0.38 0.491
pontoporeia_femorata 0.0305 0.0636 0.48 0 0.56 0.522
axinopsida_orbiculata 0.0227 0.0495 0.459 0 0.335 0.546
thracia_septentrionalis 0.0189 0.0548 0.346 0 0.235 0.565
diastylis_sculpta 0.0163 0.0353 0.461 0.0495 0.207 0.582
nephtys_caeca 0.0157 0.0381 0.412 0 0.16 0.598
diastylis_rathkei 0.0157 0.0399 0.393 0 0.237 0.614
lamprops_fuscatus 0.0144 0.0284 0.507 0 0.233 0.629
praxillella_praetermissa 0.014 0.054 0.26 0 0.158 0.644
scoloplos_armiger 0.0132 0.0399 0.331 0.0785 0.0713 0.657
hediste_diversicolor 0.0126 0.0271 0.463 0 0.225 0.67
strongylocentrotus_sp 0.0124 0.0331 0.373 0.157 0 0.683
polynoidae_spp 0.0113 0.0308 0.369 0 0.191 0.695
SIMPER results between clusters 3 and 5 (mean between-group Bray-Curtis dissimilarity: 0.998)
  average sd ratio ava avb cumsum
echinarachnius_parma 0.109 0.0537 2.03 1.85 0 0.109
eudorellopsis_integra 0.0913 0.0665 1.37 0 1.65 0.201
protomedeia_grandimana 0.0751 0.0546 1.38 0 1.36 0.276
ennucula_tenuis 0.0591 0.0519 1.14 0 1.06 0.335
leucon_leucon_nasicoides 0.0499 0.0518 0.963 0 0.898 0.385
goniada_maculata 0.049 0.0378 1.3 0 0.823 0.434
maldanidae_spp 0.0474 0.0669 0.708 0 0.879 0.482
macoma_calcarea 0.0424 0.0382 1.11 0 0.77 0.524
thyasira_gouldi 0.0347 0.0468 0.74 0 0.648 0.559
bipalponephtys_neotena 0.0337 0.0368 0.915 0 0.653 0.593
pontoporeia_femorata 0.0331 0.0536 0.618 0 0.552 0.626
quasimelita_formosa 0.0306 0.0442 0.692 0 0.565 0.656
nephtys_incisa 0.0294 0.0345 0.851 0 0.536 0.686
SIMPER results between clusters 4 and 5 (mean between-group Bray-Curtis dissimilarity: 0.816)
  average sd ratio ava avb cumsum
eudorellopsis_integra 0.0584 0.0466 1.25 0.835 1.65 0.0717
protomedeia_grandimana 0.0523 0.0393 1.33 0.0971 1.36 0.136
macoma_calcarea 0.045 0.039 1.15 1.62 0.77 0.191
bipalponephtys_neotena 0.0431 0.0407 1.06 1.09 0.653 0.244
ennucula_tenuis 0.0391 0.0339 1.15 0.38 1.06 0.292
leucon_leucon_nasicoides 0.0353 0.0375 0.94 0.0289 0.898 0.335
pontoporeia_femorata 0.0342 0.0439 0.779 0.56 0.552 0.377
maldanidae_spp 0.0339 0.0465 0.73 0.0289 0.879 0.419
goniada_maculata 0.0329 0.027 1.22 0.181 0.823 0.459
thyasira_gouldi 0.0263 0.0337 0.778 0.115 0.648 0.491
pholoe_sp 0.0244 0.0258 0.945 0.576 0.304 0.521
quasimelita_formosa 0.0226 0.0315 0.717 0.0836 0.565 0.549
nephtys_incisa 0.0213 0.0247 0.864 0.0836 0.536 0.575
polynoidae_spp 0.02 0.0251 0.797 0.191 0.431 0.599
axinopsida_orbiculata 0.0183 0.0316 0.579 0.335 0.199 0.622
oligochaeta 0.0163 0.0316 0.515 0.182 0.328 0.642
protomedeia_fasciata 0.0111 0.0234 0.475 0.16 0.171 0.655
hediste_diversicolor 0.011 0.0196 0.561 0.225 0.13 0.669
thracia_septentrionalis 0.0107 0.0269 0.399 0.235 0.0487 0.682
nephtys_caeca 0.0107 0.0247 0.432 0.16 0.0969 0.695

4. Univariate regressions

We used linear models for the all regressions on diversity indices. Outliers and correlated variables were removed from these analyses. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models).

4.1. Best model selection

This step was not used here as each models are necessary.

4.2. Significative variables selection

We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, are shown on the tables below:

  • for the 0.5 mm community:
Variable (or combination) S N H J
depth + - + +
om/silt + + -
gravel + + +
sand + + -
clay + + + -
Adjusted \(R^{2}\) 0.33 0.5 0.27 0.16
Variable (or combination) S N H J
arsenic - -
cadmium - -
chromium/iron/manganese + - -
mercury - -
lead/copper/zinc + +
Adjusted \(R^{2}\) 0.18 0.49 0.07 0.02
  • for the 1 mm community:
Variable (or combination) S N H J
depth + - + +
om/silt
gravel -
sand - - -
clay - - -
Adjusted \(R^{2}\) 0.25 0.03 0.34 0.1
Variable (or combination) S N H J
arsenic
cadmium - -
chromium/iron/manganese
mercury
lead/copper/zinc + +
Adjusted \(R^{2}\) 0.08 0 0.05 0

Details of the regressions, with diagnostics and cross-validation, are summarized below.

0.5 mm community
Richness/habitat
## FULL MODEL
## Adjusted R2 is: 0.33
Fitting linear model: S ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02378 0.06373 0.3732 0.7095
depth 0.2425 0.07287 3.328 0.001097 * *
om 0.2862 0.07987 3.584 0.0004548 * * *
gravel 0.2016 0.09011 2.237 0.0267 *
sand 0.2542 0.1164 2.183 0.03054 *
clay 0.7458 0.1126 6.623 5.636e-10 * * * 
## RMSE from cross-validation: 0.8090011
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.14 1.25 1.05 1.84 1.79 

## REDUCED MODEL
## Adjusted R2 is: 0.33
Fitting linear model: S ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02378 0.06373 0.3732 0.7095
depth 0.2425 0.07287 3.328 0.001097 * *
om 0.2862 0.07987 3.584 0.0004548 * * *
gravel 0.2016 0.09011 2.237 0.0267 *
sand 0.2542 0.1164 2.183 0.03054 *
clay 0.7458 0.1126 6.623 5.636e-10 * * * 
## RMSE from cross-validation: 0.8090011
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.14 1.25 1.05 1.84 1.79

Density/habitat
## FULL MODEL
## Adjusted R2 is: 0.5
Fitting linear model: N ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.01472 0.05775 0.2548 0.7992
depth -0.09499 0.06604 -1.438 0.1524
om 0.5369 0.07238 7.418 7.649e-12 * * *
gravel 0.1221 0.08167 1.495 0.137
sand 0.5185 0.1055 4.914 2.27e-06 * * *
clay 0.8915 0.1021 8.736 3.96e-15 * * * 
## RMSE from cross-validation: 0.7850903
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.14 1.25 1.05 1.84 1.79 

## REDUCED MODEL
## Adjusted R2 is: 0.5
Fitting linear model: N ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.01472 0.05775 0.2548 0.7992
depth -0.09499 0.06604 -1.438 0.1524
om 0.5369 0.07238 7.418 7.649e-12 * * *
gravel 0.1221 0.08167 1.495 0.137
sand 0.5185 0.1055 4.914 2.27e-06 * * *
clay 0.8915 0.1021 8.736 3.96e-15 * * * 
## RMSE from cross-validation: 0.7850903
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.14 1.25 1.05 1.84 1.79

Diversity/habitat
## FULL MODEL
## Adjusted R2 is: 0.26
Fitting linear model: H ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.05663 0.06356 0.891 0.3743
depth 0.5219 0.07268 7.181 2.822e-11 * * *
om 0.01009 0.07966 0.1267 0.8993
gravel 0.156 0.08988 1.735 0.08467
sand -0.07361 0.1161 -0.6339 0.5271
clay 0.2279 0.1123 2.029 0.04422 * 
## RMSE from cross-validation: 0.8101494
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.14 1.25 1.05 1.84 1.79 

## REDUCED MODEL
## Adjusted R2 is: 0.27
Fitting linear model: H ~ depth + gravel + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.05587 0.06327 0.883 0.3786
depth 0.535 0.07073 7.565 3.221e-12 * * *
gravel 0.1584 0.08587 1.844 0.06707
clay 0.2884 0.06988 4.127 5.989e-05 * * * 
## RMSE from cross-validation: 0.8019987
Variance Inflation Factors
  depth gravel clay
VIF 1.11 1 1.12

Evenness/habitat
## FULL MODEL
## Adjusted R2 is: 0.15
Fitting linear model: J ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.05235 0.06563 0.7977 0.4263
depth 0.3094 0.07505 4.122 6.142e-05 * * *
om -0.1443 0.08226 -1.754 0.08146
gravel -0.004248 0.09281 -0.04577 0.9636
sand -0.2053 0.1199 -1.712 0.08889
clay -0.2228 0.116 -1.921 0.05654 
## RMSE from cross-validation: 0.8333639
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.14 1.25 1.05 1.84 1.79 

## REDUCED MODEL
## Adjusted R2 is: 0.16
Fitting linear model: J ~ depth + om + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.05272 0.06493 0.8119 0.4181
depth 0.3095 0.07478 4.138 5.741e-05 * * *
om -0.1432 0.07888 -1.816 0.07132
sand -0.2041 0.1168 -1.748 0.08245
clay -0.2218 0.1134 -1.956 0.05228 
## RMSE from cross-validation: 0.8267531
Variance Inflation Factors
  depth om sand clay
VIF 1.14 1.21 1.8 1.75

Richness/metals
## FULL MODEL
## Adjusted R2 is: 0.17
Fitting linear model: S ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.06047 0.0758 -0.7978 0.4264
arsenic -0.4048 0.1342 -3.017 0.003051 * *
cadmium -0.6834 0.1672 -4.087 7.426e-05 * * *
chromium -0.01251 0.138 -0.09062 0.9279
mercury -0.4632 0.1701 -2.723 0.007325 * *
lead 1.038 0.2052 5.06 1.333e-06 * * * 
## RMSE from cross-validation: 0.8958299
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.56 2.08 1.75 1.43 2.7 

## REDUCED MODEL
## Adjusted R2 is: 0.18
Fitting linear model: S ~ arsenic + cadmium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.05982 0.07518 -0.7956 0.4276
arsenic -0.4045 0.1337 -3.026 0.002957 * *
cadmium -0.6892 0.154 -4.474 1.6e-05 * * *
mercury -0.4584 0.161 -2.847 0.005086 * *
lead 1.031 0.19 5.427 2.524e-07 * * * 
## RMSE from cross-validation: 0.8902463
Variance Inflation Factors
  arsenic cadmium mercury lead
VIF 1.56 1.92 1.36 2.51

Density/metals
## FULL MODEL
## Adjusted R2 is: 0.49
Fitting linear model: N ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1098 0.05881 -1.867 0.06407
arsenic -0.5128 0.1041 -4.926 2.398e-06 * * *
cadmium -0.7432 0.1297 -5.729 6.212e-08 * * *
chromium 0.2695 0.1071 2.517 0.01301 *
mercury -0.7299 0.132 -5.529 1.588e-07 * * *
lead 1.43 0.1592 8.986 1.907e-15 * * * 
## RMSE from cross-validation: 0.7065136
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.56 2.08 1.75 1.43 2.7 

## REDUCED MODEL
## Adjusted R2 is: 0.49
Fitting linear model: N ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1098 0.05881 -1.867 0.06407
arsenic -0.5128 0.1041 -4.926 2.398e-06 * * *
cadmium -0.7432 0.1297 -5.729 6.212e-08 * * *
chromium 0.2695 0.1071 2.517 0.01301 *
mercury -0.7299 0.132 -5.529 1.588e-07 * * *
lead 1.43 0.1592 8.986 1.907e-15 * * * 
## RMSE from cross-validation: 0.7065136
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.56 2.08 1.75 1.43 2.7

Diversity/metals
## FULL MODEL
## Adjusted R2 is: 0.06
Fitting linear model: H ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.03115 0.07881 0.3952 0.6933
arsenic -0.115 0.1395 -0.8244 0.4112
cadmium -0.2287 0.1738 -1.316 0.1905
chromium -0.2563 0.1435 -1.786 0.0764
mercury 0.02354 0.1769 0.1331 0.8943
lead 0.2542 0.2133 1.192 0.2355 
## RMSE from cross-validation: 0.9303706
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.56 2.08 1.75 1.43 2.7 

## REDUCED MODEL
## Adjusted R2 is: 0.07
Fitting linear model: H ~ chromium
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0391 0.077 0.5078 0.6124
chromium -0.2812 0.08139 -3.455 0.0007288 * * * 
## RMSE from cross-validation: 0.9157629
Variance Inflation Factors
  chromium
VIF 1

Evenness/metals
## FULL MODEL
## Adjusted R2 is: 0.03
Fitting linear model: J ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.07716 0.07843 0.9838 0.3269
arsenic 0.1621 0.1388 1.167 0.2451
cadmium 0.2536 0.173 1.466 0.145
chromium -0.238 0.1428 -1.666 0.09799
mercury 0.1657 0.176 0.9411 0.3483
lead -0.3184 0.2123 -1.5 0.1359 
## RMSE from cross-validation: 0.9265784
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.56 2.08 1.75 1.43 2.7 

## REDUCED MODEL
## Adjusted R2 is: 0.02
Fitting linear model: J ~ chromium
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.05581 0.07726 0.7224 0.4713
chromium -0.1744 0.08166 -2.136 0.03445 * 
## RMSE from cross-validation: 0.9269077
Variance Inflation Factors
  chromium
VIF 1

1 mm community
Richness/habitat
## FULL MODEL
## Adjusted R2 is: 0.25
Fitting linear model: S ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.04634 0.06256 -0.7407 0.4598
depth 0.2715 0.06482 4.188 4.307e-05 * * *
om -0.04458 0.1117 -0.3991 0.6903
gravel -0.1185 0.08561 -1.384 0.1679
sand -0.4367 0.1322 -3.304 0.001141 * *
clay -0.5032 0.1403 -3.586 0.0004272 * * * 
## RMSE from cross-validation: 0.8934522
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.06 1.81 1.03 2.1 1.45 

## REDUCED MODEL
## Adjusted R2 is: 0.25
Fitting linear model: S ~ depth + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.03361 0.06184 -0.5436 0.5874
depth 0.2678 0.06475 4.136 5.298e-05 * * *
sand -0.3869 0.07612 -5.083 8.825e-07 * * *
clay -0.4563 0.114 -4.001 9.001e-05 * * * 
## RMSE from cross-validation: 0.8673699
Variance Inflation Factors
  depth sand clay
VIF 1.06 1.21 1.18

Density/habitat
## FULL MODEL
## Adjusted R2 is: 0.02
Fitting linear model: N ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0211 0.07296 -0.2892 0.7727
depth -0.1713 0.07559 -2.266 0.02458 *
om -0.09757 0.1303 -0.749 0.4548
gravel 0.002914 0.09984 0.02919 0.9767
sand -0.29 0.1542 -1.881 0.06147
clay -0.3748 0.1636 -2.291 0.02309 * 
## RMSE from cross-validation: 1.008745
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.06 1.81 1.03 2.1 1.45 

## REDUCED MODEL
## Adjusted R2 is: 0.03
Fitting linear model: N ~ depth + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.01888 0.07186 -0.2627 0.7931
depth -0.1719 0.07525 -2.285 0.02341 *
sand -0.1969 0.08846 -2.226 0.02718 *
clay -0.3066 0.1325 -2.314 0.02174 * 
## RMSE from cross-validation: 1.002598
Variance Inflation Factors
  depth sand clay
VIF 1.06 1.21 1.18

Diversity/habitat
## FULL MODEL
## Adjusted R2 is: 0.34
Fitting linear model: H ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.02566 0.05866 -0.4374 0.6623
depth 0.4303 0.06078 7.079 2.765e-11 * * *
om 0.06608 0.1047 0.631 0.5288
gravel -0.1077 0.08027 -1.341 0.1815
sand -0.2444 0.124 -1.971 0.05013
clay -0.2757 0.1316 -2.096 0.03742 * 
## RMSE from cross-validation: 0.8438226
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.06 1.81 1.03 2.1 1.45 

## REDUCED MODEL
## Adjusted R2 is: 0.34
Fitting linear model: H ~ depth + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.02847 0.0584 -0.4875 0.6265
depth 0.4311 0.06067 7.105 2.348e-11 * * *
gravel -0.119 0.0781 -1.524 0.1292
sand -0.3082 0.07143 -4.315 2.564e-05 * * *
clay -0.3236 0.1073 -3.017 0.002901 * * 
## RMSE from cross-validation: 0.8399062
Variance Inflation Factors
  depth gravel sand clay
VIF 1.06 1.01 1.21 1.18

Evenness/habitat
## FULL MODEL
## Adjusted R2 is: 0.08
Fitting linear model: J ~ depth + om + gravel + sand + clay
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.01566 0.06839 0.2289 0.8192
depth 0.2928 0.07086 4.133 5.383e-05 * * *
om 0.02954 0.1221 0.2419 0.8091
gravel 0.008159 0.09359 0.08718 0.9306
sand -0.04328 0.1445 -0.2995 0.7649
clay -0.02071 0.1534 -0.135 0.8927 
## RMSE from cross-validation: 0.9552492
Variance Inflation Factors
  depth om gravel sand clay
VIF 1.06 1.81 1.03 2.1 1.45 

## REDUCED MODEL
## Adjusted R2 is: 0.1
Fitting linear model: J ~ depth
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.01448 0.0664 0.2181 0.8276
depth 0.3126 0.06602 4.735 4.225e-06 * * * 
## RMSE from cross-validation: 0.9358617
Variance Inflation Factors
  depth
VIF 1

Richness/metals
## FULL MODEL
## Adjusted R2 is: 0.07
Fitting linear model: S ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.06323 0.08253 0.7661 0.4451
arsenic -0.1466 0.1335 -1.098 0.2742
cadmium -0.5481 0.1606 -3.413 0.0008755 * * *
chromium 0.01217 0.1638 0.07431 0.9409
mercury -0.001392 0.1293 -0.01077 0.9914
lead 0.4503 0.2727 1.651 0.1013 
## RMSE from cross-validation: 0.9298714
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.65 1.85 1.89 1.48 3.17 

## REDUCED MODEL
## Adjusted R2 is: 0.08
Fitting linear model: S ~ cadmium + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.06172 0.08196 0.7531 0.4529
cadmium -0.5082 0.149 -3.409 0.000881 * * *
lead 0.3104 0.1476 2.103 0.03751 * 
## RMSE from cross-validation: 0.9219954
Variance Inflation Factors
  cadmium lead
VIF 1.73 1.73

Density/metals
## FULL MODEL
## Adjusted R2 is: -0.01
Fitting linear model: N ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.01452 0.09066 0.1602 0.873
arsenic -0.1569 0.1467 -1.07 0.287
cadmium -0.161 0.1764 -0.9126 0.3633
chromium -0.2011 0.18 -1.117 0.2661
mercury -0.1079 0.142 -0.7597 0.4489
lead 0.4704 0.2995 1.57 0.1189 
## RMSE from cross-validation: 1.030112
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.65 1.85 1.89 1.48 3.17 

## REDUCED MODEL
## Adjusted R2 is: 0
Fitting linear model: N ~ 1
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.01849 0.08981 0.2058 0.8373 
## RMSE from cross-validation: 1.011527

Quitting from lines 616-618 (C1_analyses_B.Rmd) Error in Qr$qr[p1, p1, drop = FALSE] : indice hors limites De plus : There were 33 warnings (use warnings() to see them)

Diversity/metals
## FULL MODEL
## Adjusted R2 is: 0.04
Fitting linear model: H ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.09394 0.08016 1.172 0.2435
arsenic -0.1326 0.1297 -1.023 0.3086
cadmium -0.4559 0.156 -2.923 0.004141 * *
chromium 0.006562 0.1591 0.04124 0.9672
mercury 0.03018 0.1255 0.2404 0.8104
lead 0.3954 0.2648 1.493 0.1381 
## RMSE from cross-validation: 0.8995954
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.65 1.85 1.89 1.48 3.17 

## REDUCED MODEL
## Adjusted R2 is: 0.05
Fitting linear model: H ~ cadmium + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.09219 0.07962 1.158 0.2492
cadmium -0.4268 0.1448 -2.948 0.003831 * *
lead 0.2923 0.1434 2.038 0.04365 * 
## RMSE from cross-validation: 0.8942355
Variance Inflation Factors
  cadmium lead
VIF 1.73 1.73

Evenness/metals
## FULL MODEL
## Adjusted R2 is: -0.04
Fitting linear model: J ~ arsenic + cadmium + chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.06202 0.07961 0.779 0.4375
arsenic -0.05693 0.1288 -0.4421 0.6592
cadmium -0.04309 0.1549 -0.2782 0.7813
chromium 0.05701 0.158 0.3607 0.7189
mercury 0.03799 0.1247 0.3047 0.7611
lead -0.004916 0.263 -0.01869 0.9851 
## RMSE from cross-validation: 0.8928066
Variance Inflation Factors
  arsenic cadmium chromium mercury lead
VIF 1.65 1.85 1.89 1.48 3.17 

## REDUCED MODEL
## Adjusted R2 is: 0
Fitting linear model: J ~ 1
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.06244 0.07799 0.8006 0.4249 
## RMSE from cross-validation: 0.873627

Quitting from lines 640-642 (C1_analyses_B.Rmd) Error in Qr\(qr[p1, p1, drop = FALSE] : indice hors limites De plus : Warning messages: 1: In CVlm(data = lm_out\)model, form.lm = lm_out, m = 5, printit = F) :

As there is >1 explanatory variable, cross-validation predicted values for a fold are not a linear function of corresponding overall predicted values. Lines that are shown for the different folds are approximate

2: In CVlm(data = lm_out$model, form.lm = lm_out, m = 5, printit = F) :

As there is >1 explanatory variable, cross-validation predicted values for a fold are not a linear function of corresponding overall predicted values. Lines that are shown for the different folds are approximate

3: In CVlm(data = lm_out$model, form.lm = lm_out, m = 5, printit = F) :

As there is >1 explanatory variable, cross-validation predicted values for a fold are not a linear function of corresponding overall predicted values. Lines that are shown for the different folds are approximate

4: In CVlm(data = lm_out$model, form.lm = lm_out, m = 5, printit = F) :

As there is >1 explanatory variable, cross-validation predicted values for a fold are not a linear function of corresponding overall predicted values. Lines that are shown for the different folds are approximate

5. Multivariate regression

Independant variables are habitat parameters or heavy metal concentrations, dependant variables are species abundances for each community. Variables have been standardized by mean and standard-deviation, and outliers and correlated variables have been excluded.

This analysis has been done on PRIMER, with a DistLM to identify the variables that explain the most the community variability and with a dbRDA to plot the results.

0.5 mm community
Habitat

Variables selected by the DistLM procedure have a \(R^{2}\) of 0.27.

Metals

Variables selected by the DistLM procedure have a \(R^{2}\) of 0.18.

1 mm community
Habitat

Variables selected by the DistLM procedure have a \(R^{2}\) of 0.14.

Metals

Variables selected by the DistLM procedure have a \(R^{2}\) of 0.07.

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